SoroushVahidi/scidocs huggingface dataset:
Paddy diseases identification with texture analysis using fractal descriptors based on fourier spectrum
(2013)

The efforts to increasing the quantity and quality of rice production are obstructed by the paddy disease. This research attempted to identify the four major paddy diseases in Indonesia (leaf blast, brown spot, bacterial leaf blight, and tungro) using fractal descriptors to analyze the texture of the lesions. The lesion images were extracted manually. The descriptors of `S' component of each lesion images then used in classification process using probabilistic neural networks. This techniques achieved at least 83.00% accuracy when identifying the diseases. This method has a potential to be used as one of the feature if it combined with other features, especially when two diseases with relatively same color involved.
Proceedings of the ACL-2012 Special Workshop on Rediscovering 50 Years of Discoveries
(2012)

The ACL Anthology Network (AAN)1 is a comprehensive manually curated networked database of citations and collaborations in the field of Computational Linguistics. Each citation edge in AAN is associated with one or more citing sentences. A citing sentence is one that appears in a scientific article and contains an explicit reference to another article. In this paper, we shed the light on the usefulness of AAN citing sentences for understanding research trends and summarizing previous discoveries and contributions. We also propose and motivate several different uses and applications of citing sentences.
Flexible UHF/VHF Vivaldi antenna for broadband and gas balloon applications
(2017)

A Vivaldi flexible antenna with a −6 dB bandwidth from 150 MHz to 2000 MHz is introduced. The antenna is fabricated on a 60∗60 cm2 silicone substrate. In this paper, we present the design, realization and performances of this wide band and directive antenna. The proposed structure is lightweight, easy to realize and does not require any matching network. The targeted application is the radio-localization of signal source emissions from helium gas balloon. Six antennas are integrated on the bottom side of the balloon and the information is recovered with a cable that serves to its stabilization.
A survey of trust computation models for service management in internet of things systems
(2017)

In this paper we survey trust computation models for Internet of things (IoT) systems for the purpose of service management, i.e., whether or not to select an IoT device as a service provider. Future IoT systems will connect the physical world into cyberspace everywhere and everything via billions of smart objects, and are expected to have a high economic impact. To date there is little work on trust computation in IoT environments for service management, especially for dealing with misbehaving owners of IoT devices that provide services to other IoT devices in the system. Our approach is to classify existing trust computation models for service management in IoT systems based on five essential design dimensions for a trust computation model: trust composition, trust propagation, trust aggregation, trust update, and trust formation. We summarize pros and cons of each dimension’s options, and highlight the effectiveness of defense mechanisms against malicious attacks. We also summarize the most, least, and little visited trust computation techniques in the literature and provide insight on the effectiveness of trust computation techniques as applying to IoT systems. Finally, we identify gaps in IoT trust computation research and suggest future research directions.
Trust management for the internet of things and its application to service composition
(2012)

The Internet of Things (IoT) integrates a large amount of everyday life devices from heterogeneous network environments, bringing a great challenge into security and reliability management. Recognizing that the smart objects in IoT are most likely human-carried or human-operated devices, we propose a scalable trust management protocol for IoT, with the emphasis on social relationships. We consider multiple trust properties including honesty, cooperativeness, and community-interest to account for social interaction. Each node performs trust evaluation towards a limited set of devices of its interest only. The trust management protocol is event-driven upon the occurrence of a social encounter or interaction event, and trust is aggregated using both direct observations and indirect recommendations. We analyze the effect of trust parameters on trust assessment accuracy and trust convergence time. Our results show that there exists a trade-off between trust assessment accuracy vs. trust convergence time in the presence of false recommendations attacks performed by malicious nodes. We demonstrate the effectiveness of the proposed trust management protocol with a trust-based service composition application. Our results indicate that trust-based service composition significantly outperforms non-trust-based (random) service composition and its performance approaches the maximum achievable performance with global knowledge.
Embedded Interaction: Interacting with the Internet of Things
(2010)

The Internet of Things assumes that objects have digital functionality and can be identified and tracked automatically. The main goal of embedded interaction is to look at new opportunities that arise for interactive systems and the immediate value users gain. The authors developed various prototypes to explore novel ways for human-computer interaction (HCI), enabled by the Internet of Things and related technologies. Based on these experiences, they derive a set of guidelines for embedding interfaces into people's daily lives.
TRM-IoT: A trust management model based on fuzzy reputation for internet of things
(2011)

Since a large scale Wireless Sensor Network (WSN) is to be completely integrated into Internet as a core part of Internet of Things (IoT) or Cyber Physical System (CPS), it is necessary to consider various security challenges that come with IoT/CPS, such as the detection of malicious attacks. Sensors or sensor embedded things may establish direct communication between each other using 6LoWPAN protocol. A trust and reputation model is recognized as an important approach to defend a large distributed sensor networks in IoT/CPS against malicious node attacks, since trust establishment mechanisms can stimulate collaboration among distributed computing and communication entities, facilitate the detection of untrustworthy entities, and assist decision-making process of various protocols. In this paper, based on in-depth understanding of trust establishment process and quantitative comparison among trust establishment methods, we present a trust and reputation model TRM-IoT to enforce the cooperation between things in a network of IoT/CPS based on their behaviors. The accuracy, robustness and lightness of the proposed model is validated through a wide set of simulations.
Learned Optimizers that Scale and Generalize
(2017)

Learning to learn has emerged as an important direction for achieving artificial intelligence. Two of the primary barriers to its adoption are an inability to scale to larger problems and a limited ability to generalize to new tasks. We introduce a learned gradient descent optimizer that generalizes well to new tasks, and which has significantly reduced memory and computation overhead. We achieve this by introducing a novel hierarchical RNN architecture, with minimal perparameter overhead, augmented with additional architectural features that mirror the known structure of optimization tasks. We also develop a meta-training ensemble of small, diverse optimization tasks capturing common properties of loss landscapes. The optimizer learns to outperform RMSProp/ADAM on problems in this corpus. More importantly, it performs comparably or better when applied to small convolutional neural networks, despite seeing no neural networks in its meta-training set. Finally, it generalizes to train Inception V3 and ResNet V2 architectures on the ImageNet dataset for thousands of steps, optimization problems that are of a vastly different scale than those it was trained on. We release an open source implementation of the meta-training algorithm.
Rational Trust Modeling
(2017)

Trust models are widely used in various computer science disciplines. The primary purpose of a trust model is to continuously measure the trustworthiness of a set of entities based on their behaviors. In this article, the novel notion of rational trust modeling is introduced by bridging trust management and game theory. Note that trust models/reputation systems have been used in game theory (e.g., repeated games) for a long time, however, game theory has not been utilized in the process of trust model construction; this is the novelty of our approach. In our proposed setting, the designer of a trust model assumes that the players who intend to utilize the model are rational/selfish, i.e., they decide to become trustworthy or untrustworthy based on the utility that they can gain. In other words, the players are incentivized (or penalized) by the model itself to act properly. The problem of trust management can be then approached by game theoretical analyses and solution concepts such as Nash equilibrium. Although rationality might be built-in in some existing trust models, we intend to formalize the notion of rational trust modeling from the designer’s perspective. This approach will result in two fascinating outcomes. First of all, the designer of a trust model can incentivize trustworthiness in the first place by incorporating proper parameters into the trust function, which can be later utilized among selfish players in strategic trust-based interactions (e.g., e-commerce scenarios). Furthermore, using a rational trust model, we can prevent many well-known attacks on trust models. These two prominent properties also help us to predict the behavior of the players in subsequent steps by game theoretical analyses.
Crosslingual Distributed Representations of Words
(2012)

Distributed representations of words have proven extremely useful in numerous natural language processing tasks. Their appeal is that they can help alleviate data sparsity problems common to supervised learning. Methods for inducing these representations require only unlabeled language data, which are plentiful for many natural languages. In this work, we induce distributed representations for a pair of languages jointly. We treat it as a multitask learning problem where each task corresponds to a single word, and task relatedness is derived from co-occurrence statistics in bilingual parallel data. These representations can be used for a number of crosslingual learning tasks, where a learner can be trained on annotations present in one language and applied to test data in another. We show that our representations are informative by using them for crosslingual document classification, where classifiers trained on these representations substantially outperform strong baselines when applied to a new language.
Measuring the impact of opening the London shared bicycle scheme to casual users
(2012)

0968-090X/$ see front matter 2011 Published b doi:10.1016/j.trc.2011.12.004 ⇑ Corresponding author. Tel.: +44 20 7679 7214. E-mail address: n.lathia@cs.ucl.ac.uk (N. Lathia). The increasing availability of sensor data in urban areas now offers the opportunity to perform continuous evaluations of transport systems and measure the effects of policy changes, in an empirical, large-scale, and non-invasive way. In this paper, we study one such example: the effect of changing the user-access policy in the London Barclays Cycle Hire scheme. When the scheme was launched in July 2010, users were required to apply for a key to access to the system. By December 2010, this policy was overridden in order to allow for ‘‘casual’’ usage, so that anyone in possession of a debit or credit card could gain access. While the transport authority measured the policy shift’s success by the increased number of trips, we set out to investigate how the change affected the system’s usage throughout the city. We present an extensive analysis of station data collected from the scheme’s web site both preand post-policy change, showing how differences in both global and local behaviour can be measured, and how the policy change correlates with a variety of effects observed around the city. We find that, as expected, quicker access to the system correlates with greater week end usage; it also reinforces the week-day commuting trend. In both the preand post-change periods, the geographic distribution of activity at individual stations forms concentric circles around central London. However, upon policy change, a number of stations undergo a complete usage change, now exhibiting an opposite trend with respect to that which they had prior to the policy change. 2011 Published by Elsevier Ltd.
Boosted Regression Active Shape Models
(2007)

We present an efficient method of fitting a set of local feature models to an image within the popular Active Shape Model (ASM) framework [3]. We compare two different types of non-linear boosted feature models trained using GentleBoost [9]. The first type is a conventional feature detector classifier, which learns a discrimination function between the appearance of a feature and the local neighbourhood. The second local model type is a boosted regression predictor which learns the relationship between the local neighbourhood appearance and the displacement from the true feature location. At run-time the second regression model is much more efficient as only the current feature patch needs to be processed. We show that within the local iterative search of the ASM the local feature regression provides improved localisation on two publicly available human face test sets as well as increasing the search speed by a factor of eight.
3D Printing for the Rapid Prototyping of Structural Electronics
(2014)

In new product development, time to market (TTM) is critical for the success and profitability of next generation products. When these products include sophisticated electronics encased in 3D packaging with complex geometries and intricate detail, TTM can be compromised - resulting in lost opportunity. The use of advanced 3D printing technology enhanced with component placement and electrical interconnect deposition can provide electronic prototypes that now can be rapidly fabricated in comparable time frames as traditional 2D bread-boarded prototypes; however, these 3D prototypes include the advantage of being embedded within more appropriate shapes in order to authentically prototype products earlier in the development cycle. The fabrication freedom offered by 3D printing techniques, such as stereolithography and fused deposition modeling have recently been explored in the context of 3D electronics integration - referred to as 3D structural electronics or 3D printed electronics. Enhanced 3D printing may eventually be employed to manufacture end-use parts and thus offer unit-level customization with local manufacturing; however, until the materials and dimensional accuracies improve (an eventuality), 3D printing technologies can be employed to reduce development times by providing advanced geometrically appropriate electronic prototypes. This paper describes the development process used to design a novelty six-sided gaming die. The die includes a microprocessor and accelerometer, which together detect motion and upon halting, identify the top surface through gravity and illuminate light-emitting diodes for a striking effect. By applying 3D printing of structural electronics to expedite prototyping, the development cycle was reduced from weeks to hours.
Systems of Systems Engineering: Basic Concepts, Model-Based Techniques, and Research Directions
(2015)

The term “System of Systems” (SoS) has been used since the 1950s to describe systems that are composed of independent constituent systems, which act jointly towards a common goal through the synergism between them. Examples of SoS arise in areas such as power grid technology, transport, production, and military enterprises. SoS engineering is challenged by the independence, heterogeneity, evolution, and emergence properties found in SoS. This article focuses on the role of model-based techniques within the SoS engineering field. A review of existing attempts to define and classify SoS is used to identify several dimensions that characterise SoS applications. The SoS field is exemplified by a series of representative systems selected from the literature on SoS applications. Within the area of model-based techniques the survey specifically reviews the state of the art for SoS modelling, architectural description, simulation, verification, and testing. Finally, the identified dimensions of SoS characteristics are used to identify research challenges and future research areas of model-based SoS engineering.
EnsembleHMD: Accurate Hardware Malware Detectors with Specialized Ensemble Classifiers
(2018)

Hardware-based malware detectors (HMDs) are a promising new approach to defend against malware. HMDs collect low-level architectural features and use them to classify malware from normal programs. With simple hardware support, HMDs can be always on, operating as a first line of defense that prioritizes the application of more expensive and more accurate software-detector. In this paper, our goal is to increase the accuracy of HMDs, to improve detection, and reduce overhead. We use specialized detectors targeted towards a specific type of malware to improve the detection of each type. Next, we use ensemble learning techniques to improve the overall accuracy by combining detectors. We explore detectors based on logistic regression (LR) and neural networks (NN). The proposed detectors reduce the false-positive rate by more than half compared to using a single detector, while increasing their sensitivity. We develop metrics to estimate detection overhead; the proposed detectors achieve more than 16.6x overhead reduction during online detection compared to an idealized software-only detector, with an 8x improvement in relative detection time. NN detectors outperform LR detectors in accuracy, overhead (by 40%), and time-to-detection of the hardware component (by 5x). Finally, we characterize the hardware complexity by extending an open-core and synthesizing it on an FPGA platform, showing that the overhead
Crunch Time: The Reasons and Effects of Unpaid Overtime in the Games Industry
(2017)

The games industry is notorious for its intense work ethics with uncompensated overtime and weekends at the office, also known as crunch or crunch time. Since crunch time is so common within the industry, is it possible that the benefits of crunch time outweigh the disadvantages? By studying postmortems and conducting interviews with employees in the industry, we aim to characterise crunch time and discover its effects on the industry. We provide a classification of crunch, i.e., four types of crunch which all have distinct characteristics and affect the product, employees and schedule in various ways. One of the crunch types stands out from the others by only having positive effects on product and schedule. A characteristic that all of the types have in common is an increase in stress levels amongst the employees. We identify a set of reasons for crunch and show that crunch is less pronounced in game studios where prioritisation of features is a regular practice.
How to Train Your Browser: Preventing XSS Attacks Using Contextual Script Fingerprints
(2016)

Cross-Site Scripting (XSS) is one of the most common web application vulnerabilities. It is therefore sometimes referred to as the “buffer overflow of the web.” Drawing a parallel from the current state of practice in preventing unauthorized native code execution (the typical goal in a code injection), we propose a script whitelisting approach to tame JavaScript-driven XSS attacks. Our scheme involves a transparent script interception layer placed in the browser’s JavaScript engine. This layer is designed to detect every script that reaches the browser, from every possible route, and compare it to a list of valid scripts for the site or page being accessed; scripts not on the list are prevented from executing. To avoid the false positives caused by minor syntactic changes (e.g., due to dynamic code generation), our layer uses the concept of contextual fingerprints when comparing scripts.
 Contextual fingerprints are identifiers that represent specific elements of a script and its execution context. Fingerprints can be easily enriched with new elements, if needed, to enhance the proposed method’s robustness. The list can be populated by the website’s administrators or a trusted third party. To verify our approach, we have developed a prototype and tested it successfully against an extensive array of attacks that were performed on more than 50 real-world vulnerable web applications. We measured the browsing performance overhead of the proposed solution on eight websites that make heavy use of JavaScript. Our mechanism imposed an average overhead of 11.1% on the execution time of the JavaScript engine. When measured as part of a full browsing session, and for all tested websites, the overhead introduced by our layer was less than 0.05%. When script elements are altered or new scripts are added on the server side, a new fingerprint generation phase is required. To examine the temporal aspect of contextual fingerprints, we performed a short-term and a long-term experiment based on the same websites. The former, showed that in a short period of time (10 days), for seven of eight websites, the majority of valid fingerprints stay the same (more than 92% on average). The latter, though, indicated that, in the long run, the number of fingerprints that do not change is reduced. Both experiments can be seen as one of the first attempts to study the feasibility of a whitelisting approach for the web.
Affordance detection of tool parts from geometric features
(2015)

As robots begin to collaborate with humans in everyday workspaces, they will need to understand the functions of tools and their parts. To cut an apple or hammer a nail, robots need to not just know the tool's name, but they must localize the tool's parts and identify their functions. Intuitively, the geometry of a part is closely related to its possible functions, or its affordances. Therefore, we propose two approaches for learning affordances from local shape and geometry primitives: 1) superpixel based hierarchical matching pursuit (S-HMP); and 2) structured random forests (SRF). Moreover, since a part can be used in many ways, we introduce a large RGB-Depth dataset where tool parts are labeled with multiple affordances and their relative rankings. With ranked affordances, we evaluate the proposed methods on 3 cluttered scenes and over 105 kitchen, workshop and garden tools, using ranked correlation and a weighted F-measure score [26]. Experimental results over sequences containing clutter, occlusions, and viewpoint changes show that the approaches return precise predictions that could be used by a robot. S-HMP achieves high accuracy but at a significant computational cost, while SRF provides slightly less accurate predictions but in real-time. Finally, we validate the effectiveness of our approaches on the Cornell Grasping Dataset [25] for detecting graspable regions, and achieve state-of-the-art performance.
Integrated path planning and dynamic steering control for autonomous vehicles
(1986)

A method is presented for combining two previously proposed algorithms for path-planning and dynamic steering control into a computationally feasible scheme for real-time feedback control of autonomous vehicles in uncertain environments. In the proposed approach to vehicle guidance and control, Path Relaxation is used to compute critical points along a globally desirable path using a priori information and sensor data'. Generalized potential fields are then used for local feedback control to drive the vehicle along a collision-free path using the critical points as subgoals'. Simulation results are presented to demonstrate the control scheme.